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      Effectiveness of Body Roundness Index (BRI) and a Body Shape Index (ABSI) in Predicting Hypertension: A Systematic Review and Meta-Analysis of Observational Studies

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          Abstract

          Introduction: The body roundness index (BRI) and a body shape index (ABSI) are novel anthropometric indices established to determine both the amount visceral adipose tissue and body fat. Objective: to investigate whether BRI and ABSI are better predictors of hypertension than body mass index (BMI), waist circumference (WC) or waist-to-height ratio (WHtR). Methods: A systematic search was conducted in the Scopus, PubMed and Web of Science databases up until 31 December 2020. Results: The estimated pooled area under curve [AUC (95% CI)] for BRI [0.67 (0.65–0.70)] for the prediction of hypertension were superior to that of ABSI (0.58 (0.56–0.60)), similar to that of BMI [0.67 (0.64–0.69)], and lower than those WC [0.68 (0.66–0.70)] and WHtR [0.68 (0.66–0.71)]. Nevertheless, the difference of BRI compared to WC and WHtR in the context of predicting hypertension was non-significant. ABSI was significantly lower (p < 0.05) than BRI, BMI, WC and WHtR. Similar findings were observed with the summary receiver operating characteristic curve (AUC-SROC). There were no significant differences between subgroups according to type of population or diagnostic criteria of hypertension. The diagnostic odds ratio (dORs) proved that increased BRI and ABSI were related with an elevated hypertension risk. Conclusions: BRI and ABSI have discriminatory power for hypertension in adult women and men from different populations. Although, WHtR and WC provided the best performance when assessing hypertension, no significant differences were found for BRI. Finally, BRI was significantly better predictor of hypertension than ABSI.

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          Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement.

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            Bias in meta-analysis detected by a simple, graphical test

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              Quantifying heterogeneity in a meta-analysis.

              The extent of heterogeneity in a meta-analysis partly determines the difficulty in drawing overall conclusions. This extent may be measured by estimating a between-study variance, but interpretation is then specific to a particular treatment effect metric. A test for the existence of heterogeneity exists, but depends on the number of studies in the meta-analysis. We develop measures of the impact of heterogeneity on a meta-analysis, from mathematical criteria, that are independent of the number of studies and the treatment effect metric. We derive and propose three suitable statistics: H is the square root of the chi2 heterogeneity statistic divided by its degrees of freedom; R is the ratio of the standard error of the underlying mean from a random effects meta-analysis to the standard error of a fixed effect meta-analytic estimate, and I2 is a transformation of (H) that describes the proportion of total variation in study estimates that is due to heterogeneity. We discuss interpretation, interval estimates and other properties of these measures and examine them in five example data sets showing different amounts of heterogeneity. We conclude that H and I2, which can usually be calculated for published meta-analyses, are particularly useful summaries of the impact of heterogeneity. One or both should be presented in published meta-analyses in preference to the test for heterogeneity. Copyright 2002 John Wiley & Sons, Ltd.
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                Author and article information

                Contributors
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                Journal
                IJERGQ
                International Journal of Environmental Research and Public Health
                IJERPH
                MDPI AG
                1660-4601
                November 2021
                November 04 2021
                : 18
                : 21
                : 11607
                Article
                10.3390/ijerph182111607
                34770120
                f5a173eb-7057-4c9c-a796-d02794f7324f
                © 2021

                https://creativecommons.org/licenses/by/4.0/

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